使用c API通过分类器模型运行连续图像后，tensorflow崩溃，错误为：“ EXT-fs错误”

问题描述

我使用tensorflow的c api遇到了一个问题。基本上，我想做的是通过图像分类器模型（在本例中为mobilenetv2模型）运行图像，并为每个图像计算特征向量。下面的代码是一个简单的程序，它将目录作为参数（包含图像的目录）。该代码工作正常，除了它会随机阻塞并使系统崩溃。永远不要在同一文件上。单独提交时，它在工作时崩溃。通常，该目录包含大量文件，但是它确实以某种规律性出现。我已将该程序简化为重现该错误的基本要点。

系统死机，出现类似以下错误：

EXT4-fs error (device sda1): ext4_find_entry:1456: inode #390913: comm systemd-journal: reading directory lblock 0

唯一可能的是硬重启。我本来要提交一个错误报告，但是我想先在这里尝试。如果有人有任何见解，将不胜感激。

我的系统信息是：

Linux 4.19.0-10-amd64 #1 SMP Debian 4.19.132-1 (2020-07-24) x86_64 GNU/Linux

您可以编译以下代码：

g++ -otest test.cpp -g -O0 -Wall -lboost_filesystem -lboost_system -lopencv_core -lopencv_imgproc -lopencv_imgcodecs -ltensorflow

然后通过向其传递包含图像的目录来运行：./test / path / to / image / dir

有一个model_file字符串变量指向mobilenetv2张量流模型。只需将模型放在目录中，或编辑代码以获取正确的路径。此处提供模型：

https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz

#include <iostream>
#include <cstdlib>
#include <string>
#include <cassert>
#include <boost/filesystem.hpp>
#include <opencv2/core/core_c.h>
#include <opencv2/imgproc/imgproc_c.h>
#include <opencv2/imgcodecs/imgcodecs_c.h>
#include <tensorflow/c/c_api.h>

using namespace std;
using namespace cv;

namespace fs=boost::filesystem;

static const string model_file = "mobilenet_v2_1.4_224_frozen.pb";
static const string input_layer = "input";
static const string output_layer = "MobilenetV2/Logits/AvgPool";

static int Height = 224;
static int Width = 224;

void NoOpDeAlloc(void *data,size_t len,void *arg){}

void freebuffer(void *data,size_t len){
    free(data);
}


CvMat* load_image(const string &filename,const int height,const int width){

    CvMat *src = cvLoadImageM(filename.c_str(),CV_LOAD_IMAGE_COLOR);
    if (src == NULL) return NULL;

    CvMat *img_prime = cvCreateMat(src->rows,src->cols,CV_32FC3);
    CvMat *img_resized = cvCreateMat(height,width,CV_32FC3);
    CvMat *dst = cvCreateMat(height,CV_32FC3);
    CvMat *div = cvCreateMat(height,CV_32FC3);

    /* convert to float type */
    cvConvertScale(src,img_prime,1,0);

    /* resize to heightxwidth */
    cvResize(img_prime,img_resized,CV_INTER_CUBIC);

    /* normalize values */
    CvScalar norm_constant = {255,255,255};
    cvSet(div,norm_constant,NULL);
    cvDiv(img_resized,div,dst,1);

    cvReleaseMat(&src);
    cvReleaseMat(&img_resized);
    cvReleaseMat(&img_prime);
    cvReleaseMat(&div);
    return dst;
}

TF_Buffer* readfromfile(const string &filename){
    FILE *file = fopen(filename.c_str(),"rb");
    if (file == NULL) return NULL;
    
    fseek(file,SEEK_END);
    size_t file_size = ftell(file);
    fseek(file,SEEK_SET);
    assert(file_size > 0);
    
    unsigned char *data = (unsigned char*)malloc(file_size);
    size_t n = fread(data,file_size,file);
    assert(n == file_size);

    TF_Buffer *buf = TF_NewBuffer();
    buf->data = data;
    buf->length = file_size;
    buf->data_deallocator = freebuffer;
    fclose(file);
    return buf;
}


int ProcessFiles(const fs::path &dirname){
    fs::recursive_directory_iterator dir(dirname),end;

    int ndims = 4;
    int64_t dims[4] = { 1,Height,Width,3};

    cout << "TF Model graph setup" << endl;
    TF_Graph *graph = TF_NewGraph();
    TF_Status *status = TF_NewStatus();

    TF_ImportGraphDefOptions *opts = TF_NewImportGraphDefOptions();
    TF_Buffer *graph_def = readfromfile(model_file);
    assert(graph_def != NULL);

    TF_GraphImportGraphDef(graph,graph_def,opts,status);
    assert(TF_GetCode(status) == TF_OK);
    
    TF_Sessionoptions *sess_opts = TF_NewSessionoptions();
    TF_Session *sess = TF_NewSession(graph,sess_opts,status);
    TF_Tensor *img_tensor;
    TF_Tensor *output_tensor;
    TF_Output input = { TF_GraphOperationByName(graph,input_layer.c_str()),0 };
    TF_Output output = { TF_GraphOperationByName(graph,output_layer.c_str()),0 };
    assert(input.oper != NULL);
    assert(output.oper != NULL);
    
    TF_DeleteBuffer(graph_def);
    TF_DeleteSessionoptions(sess_opts);
    TF_DeleteImportGraphDefOptions(opts);

    cout << "Read files " << endl;
    
    int count = 0;
    for (;dir!=end;++dir){
        string filename = dir->path().string();
        if (fs::is_directory(dir->status())){
            cout << "directory: " << filename << endl;
        } else if (fs::is_regular_file(dir->status())){
            cout << dec << "(" << ++count << ") " << filename << endl;
            CvMat *img = load_image(filename,Width);

            int total = Height*Width*3;
            img_tensor = TF_NewTensor(TF_FLOAT,dims,ndims,(void*)img->data.fl,total*sizeof(float),NoOpDeAlloc,NULL);
            assert(img_tensor != NULL);
            
            TF_SessionRun(sess,NULL,&input,&img_tensor,&output,&output_tensor,status);

            if (TF_GetCode(status) != TF_OK){
                cout << "unale to complete session: " << TF_Message(status) << endl;
                break;
            }


            TF_DeleteTensor(img_tensor);
            TF_DeleteTensor(output_tensor);
            img_tensor = NULL;
            output_tensor = NULL;
            cvReleaseMat(&img);
        } else {
            cout << "special file: " << filename << endl;
        }
    }

    TF_DeleteGraph(graph);
    TF_DeleteSession(sess,status);
    TF_DeleteStatus(status);
    return count;
}

int main (int argc,char **argv){
    assert(argc == 2);
    string dir = argv[1];

    cout << "directory: " << dir << endl;
    
    int n = ProcessFiles(dir);
    
    cout << "Processed files: " << n << endl;
    cout << "Done" << endl;
    return 0;
}

编译： g ++ -otest test.cpp -g -O0 -Wall -lboost_filesystem -lboost_system -lopencv_core
-lopencv_imgproc -lopencv_imgcodecs -ltensorflow

解决方法

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c-api image-classification tensorflow